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barryzm's Projects

sent_compression icon sent_compression

句子压缩模型,用于去除句子不重要的部分,使得语法分析等更加精确。

senta icon senta

Baidu's open-source Sentiment Analysis System.

sentence-similarity icon sentence-similarity

问题句子相似度计算,即给定客服里用户描述的两句话,用算法来判断是否表示了相同的语义。

shukongdashi icon shukongdashi

使用知识图谱,自然语言处理,卷积神经网络等技术,基于python语言,设计了一个数控领域故障诊断专家系统

siege icon siege

Siege is an http load tester and benchmarking utility

simbert icon simbert

a bert for retrieval and generation

similarity_cal icon similarity_cal

潜在热门标题类型挖掘—文本相似度计算 根据已有的全网各个平台的最热门短视频的标题,从数据库中选取标题具有相似度最高的短视频。

simpletransformers icon simpletransformers

Transformers made simple with training, evaluation, and prediction possible with one line each. Currently supports Sequence Classification (binary, multiclass, multilabel, sentence pair), Token Classification (NER), Question Answering, Regression, Conversational AI, and Multi-Modal tasks. Built on top of the Hugging Face Transformer library.

sisyphus icon sisyphus

Scene similarity for weak object discovery & classification

skyeye icon skyeye

智能办公OA系统[SpringBoot2-快速开发平台],适用于医院,学校,中小型企业等机构的管理。Activiti5.22+动态表单实现零java代码即可做到复杂业务的流程实施,同时包含文件在线操作、日志、考勤、CRM、ERP进销存、项目、拖拽式生成问卷、日程、笔记、计划、行政等多种复杂业务功能。同时,可进行授权二开。

slcvae icon slcvae

self labeling conditional variational auto encoder

smart-retail-by-efficientdet icon smart-retail-by-efficientdet

Use efficientdet - Python version to realize intelligent retail model training, provide data sets and trained model files ~ welcome star使用Efficientdet -pytorch版实现智能零售模型训练,提供数据集与训练好的模型文件~欢迎star

smartreply icon smartreply

Unofficial port of Google's smart reply runtime (powers gmail and assistant) model to python, allowing developers to leverage intelligent smart reply as an API in Web and embedded systems that supports Linux, a loader (ld.so), a fully POSIX C++ Runtime and Python interpreter.

smoothnlp icon smoothnlp

专注于可解释的NLP技术 An NLP toolset Implemented in python & java

smp-keyword-extraction icon smp-keyword-extraction

CSDN博客的关键词提取算法,融合TF,IDF,词性,位置等多特征。该项目用于参加2017 SMP用户画像测评,排名第四,在验证集中精度为59.9%,在最终集中精度为58.7%。模型并未使用机器学习的方法,具有较强的泛化能力。

smp2019-ecdt-nlu icon smp2019-ecdt-nlu

**中文信息学会社会媒体处理专业委员会举办的2019届中文人机对话之自然语言理解竞赛

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